This invention relates to a skin pattern and fingerprint classification system for tracing and classifying features of skin patterns such as palm prints or fingerprints.
A method of fingerprint classification according to features extracted from thinned pattern lines obtained from fingerprint pictures is disclosed in a paper entitled "An Algorithm for Classification of Fingerprints Based on the Core" by Shinichiro Ito, et al., transactions of the Institute of Electronics, Information and Communication Engineers (IEICE), D-II, Vol. J73-D-II No. 10, pp. 1733-1741 (October 1990). Ito, et al., discloses a method wherein a core of a fingerprint is detected in a fingerprint image after thinning, then ridge lines around the core point are traced for obtaining features of the ridge lines. According to kinds (i.e., types) and distributions of the ridge line features, the fingerprint image can be classified, e.g., into one of four categories, Whorl, Arch, Right Loop or Left Loop.
In the prior art, a core is detected first, on an assumption that a scanning line passing on the core point should have a largest number of cross points with ridge lines. So, at a first step, cross points with ridge lines in an appropriate region of a fingerprint image, after thinning, are counted for each of a certain number of scanning lines drawn on the region. The scanning lines are drawn in a horizontal and a vertical direction and in directions rotated .pi./4 from them. At a second step, coordinates of a temporary core point are calculated from coordinates of the scanning lines having more than a certain number of cross points with ridge lines. At a third step, a region smaller than the region applied at the former steps is settled around the temporary core point thus calculated, and the first and the second steps are repeated concerning the smaller region. Thus, the core point of the fingerprint image is finally detected by repeating the first, the second and the third steps a certain number of times.
Then, in the prior art, a certain number of beginning points are considered on ridge lines around the core point thus detected. From each of the certain number of beginning points, each ridge line is traced in both directions. The tracing continues for a certain length along the ridge line for extracting feature data as coordinates of the beginning point and both ending points. The end points are determined by each of the directional tracings of the ridge line segments. By referring to the distribution of these feature data concerning the beginning points around the core, the fingerprint image is classified.
FIG. 23 is a schematic diagram illustrating the tracing of ridge lines around a core having features to be classified as a Whorl. First, a pair of beginning points are considered on a horizontal line passing through the core of the fingerprint. Then, each of a pair of ridge lines crossing with the horizontal line at the beginning points are traced in both directions for a predetermined length, and two pairs of ending points are obtained. When the distances between the ending points of the two pairs are both shorter than the distance between the beginning points, the core is defined to have a certain value of features to be classified into the Whorl.
Although a method of ridge line tracing is not specified in the above prior art, it is performed as follows. For example, when a gap is found on a ridge line, the following steps can be used.
For example, an end point is detected along a directional tracing of a ridge line, another ridge line is searched along a vertical line having the same x-coordinates as the detected end point. The ridge line trace is then continued on a ridge line found nearest from the detected end point. An example of this method of ridge line tracing is described in a paper entitled "Feature Extraction for Fingerprint Classification," by T. Ch. Malleswara Rao, Pattern Recognition, Vol. 8, pp. 181-192 (1976).
The prior art described above is based on the assumption that a scanning line on the core point has the largest number of cross points with ridge lines. However, because ridge line image is not always so clear, the scanning line having a largest number of cross points may not always pass through the core point. So, some errors may occur in the core point detection, resulting in a wrong classification of fingerprints. This classification error is a problem.
Also, in order to reject indefinite data to avoid a wrong classification that is caused by image degradation, methods of fingerprint classification based on ridge line tracing generally need complicated devices. These complicated devices have parameters that are difficult to control as to defining the rejection level and therefore is another problem. Further, when a second possibility of classification is desired to be assigned to a fingerprint image that is classified in a category with a low reliability, the assignment itself is hardly possible to be done in the prior art. In order to perform the assignment, the method is to instead use a human operator who knows the classification system very well.
Still further, as described above, there is indeterminacy in the method of ridge line tracing when, e.g., an end-point or a bifurcation of a ridge line is found in the ridge line tracing. Therefore, when a classification system based on ridge line tracing is applied for fingerprint classification, its analyzing performance is not sufficient, because it is easily affected by the curvature or the inclination of the fingerprint.